Tool-Guided Triage Probe for Error Resolution
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Solution Overview
Problem
Developers face challenges in efficiently identifying and resolving errors in computing deployments due to incomplete configuration data and varying hardware environments, leading to increased complexity and difficulty in root cause analysis and solution implementation.
Innovation Solution
A tool-guided triage probe that utilizes a machine learning model to rank historic solutions based on resource usage, allowing for iterative solution attempts and generating a triage report to improve error resolution efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If developers manually analyze errors in computing deployments with incomplete configuration data, then they can identify and resolve errors, but the process requires excessive time, processor cycles, and downtime
Solution Approach 1:
The system performs preliminary actions by pre-processing error data from multiple computing deployments and pre-generating potential solutions before an error actually occurs. When an error is detected, the system immediately retrieves pre-analyzed solutions rather than performing analysis in real-time, significantly reducing error resolution time while maintaining high productivity
Solution Approach 2:
The system creates copies of error data and solution patterns from multiple computing deployments. By copying and storing historical error data, configuration data, and successful solutions in a structured format, the system can quickly retrieve and apply proven solutions to new errors without re-analyzing from scratch, thereby reducing time loss while preserving resolution efficiency
2Measurement precision
If comprehensive configuration data are collected from multiple computing deployments, then solution accuracy improves, but the complexity of data collection and processing increases
Solution Approach 1:
The system extracts only the essential and relevant configuration data, error characteristics, and solution patterns from computing deployments, separating critical information from unnecessary details. This extraction approach maintains high solution accuracy by focusing on key diagnostic elements while reducing data collection and processing complexity by eliminating redundant information
Solution Approach 2:
The system transforms raw configuration data into standardized parameters and features that are optimized for error analysis and solution matching. By changing the parameter representation from raw configuration values to normalized error characteristics and solution indicators, the system improves measurement precision for error diagnosis while simplifying the complexity of data processing through parameter standardization
3Loss of time
If historical solutions are ranked and attempted iteratively, then the most effective solutions are applied first reducing overall resolution time, but the system complexity increases due to ranking and iterative processing
Solution Approach 1:
The system implements feedback mechanisms where the outcomes of attempted solutions are fed back into the ranking model. Successful solutions reinforce their ranking positions, while unsuccessful solutions are re-ranked or discarded. This feedback loop enables iterative improvement of solution effectiveness without requiring complex re-analysis, reducing overall resolution time while managing system complexity through learned patterns
Solution Approach 2:
The system dynamically changes ranking parameters based on solution performance metrics and error characteristics. By adjusting weighting factors, success probabilities, and relevance scores based on accumulated data, the system optimizes the order of solution attempts to minimize resolution time while maintaining manageable complexity through adaptive parameter adjustment rather than rigid complex algorithms
Data Source
AI summary
A tool-guide triage probe provides error triaging by: detecting an error in a computing deployment with incomplete configuration data known; identifying a plurality of historic solutions for the error from computing deployments with matching configuration data; determining, via a machine learning model, an initial ranking of the plurality of historic solutions based on resource usages; performing an initial highest ranked solution on the computing deployment and appending performance thereof to a triage report; in response to determining that the initial highest ranked solution was unsuccessful, determining, via the machine learning model, a subsequent ranking of the plurality of historic solutions; performing a subsequent highest ranked solution on the computing deployment and appending performance thereof to the triage report; and transmitting the triage report identifying that the initial highest ranked solution and the subsequent highest ranked solution were performed on the computing deployment.